System to determine product density

ABSTRACT

A system for determining the product density value of a zone of preferable small fungible products within an acceptable size range and potentially in an acceptable color range. The system differentiates among products even if in close contact to determine acceptable product. The system may store data for later review or for dispensing of product in real time. The system includes a scale to determine a sample&#39;s weight, a camera to image the sample, an imaging table to permit viewing of the sample, a processor to determine the number of products in the sample, and a processor to determine the density of desired product.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation-in-part of U.S. patent application Ser. No12/706,028, entitled System to determine in near real-time productdensity in a continuous dispensing product flow, filed Feb. 16, 2010,and, as a continuation-in-part thereof, claims the benefit of U.S.Provisional Patent Application No. 61/152,930 entitled “Seed countestimator” filed on Feb. 16, 2009 in the United States Patent andTrademark Office from which U.S. patent application Ser. No. 12/706,028claims priority, and which are incorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR

Not Applicable.

BACKGROUND OF THE INVENTION

1. Field of Invention

The present invention pertains to systems for providing an approximatecount of small fungible products, such as seeds and plastic pellets.More particularly the present invention relates to determining thedensity of the fungible products in a volume, potentially in anassociated or parallel product flow so the system or a separateconnected system can dispense a close approximation of a specificquantity based on the volume calculation derived from product densityrather than dispensing by estimated weight only or for assessment ofproduct received. The user now has the ability to vary the weight inorder to achieve a very accurate piece count.

2. Description of the Related Art

Processing operations for seeds provide a clear background for thepresent type of system. In traditional seed processing operations, theoperator receives bulk deliveries of the desired seed, which includeundesirable elements in each delivery; which may also include hulls,rocks, insects, plant matter, weed seeds, and pieces of desirable seeds.The operator utilizes various equipment to remove these undesirableconstituents, leaving only whole desired seeds. This may includereceiving, cleaning, treating, potentially storing, and packing seed forpurchase, typically by weight. While purchase by actual number of seedsis desirable, due to variations in source and timing, in processing toremove undesirable constituents, the number of seeds per unit weight,the seed density, varies. Additionally, when seeds of differingsuppliers are combined, the seeds received may vary in size and moisturecontent yielding much different densities from supplier to supplier. Dueto these variations, operators have historically been unable toaccurately deliver a specific number of seeds per package, where thepackage in question may range from fifty (50) pounds to ten thousand(10,000) pounds. This creates issues for purchasers, among others, whodesire to purchase a certain quantity of seeds, typically enough forseeding of a particular area but not so much as to have leftover, andoften thereafter unusable, seed. Leftover seed may be unusable becauseof storage issues, germination period, and, particularly with the riseof genetically-modified and patented seeds, most importantly legalpermissions. Thus, inconsistent seed counts can create substantialissues, sometimes providing an insufficient or wasteful quantity ofseeds when computed on the anticipated planting rate. When attempting toprovide seeds based on quantity, operators have intentionallyunderestimated the number of seeds likely to be a particular weight bagso as to guarantee purchasers receive enough seed. This, however,results in waste as unnecessary, and therefore unusable, seed isprovided to purchasers. Moreover, operators lose potential revenues oneach sale solely to ensure sufficient seeds per sale. Regulatoryauthorities are requiring the industry begin labeling the seed packagingwith the “count” or number of seeds per container. In addition theprocessor may desire to purchase seeds by the count rather than weight.While not tested for this application the invention could provideutility in this and many other bulk product handling facilities.

Attempts to provide accurate seed counts have focused on providing atrue count of seeds by processing each seed through a counter. Oneattempt at resolving this situation has provided for each single seed tobe drawn past a photoelectric sensor and individually counted. Inanother attempt, a sampling of seeds is vibrated past a series ofphotodetector cells or seed counters and individually counted, and thenweighed, to determine a theoretical mass for the desired seed count.Problematically, these systems require that each seed be actuallycounted, which results in substantial reduction in speed of processingand which does not adequately address the issue of broken seeds, and ofdistinguishing individual seeds which are larger than the standard sizefrom clusters of seeds. In another attempt in the prior art, an image ofuniformly-sized, and ideally uniformly-distributed, seeds on ahorizontal surface is processed to determine average object size andextrapolated to determine an estimated total object count for the imagedseeds. Problematically, this system provides only a estimated countbased on computer average size based on a single review and provides nomeans to limit the count being directed to a bag or other output.Moreover, the requirement of a uniform size of seeds can create issuesas seed size may vary significantly. Unequal distribution, particularlydue to clusters of seeds, skews the results.

Additionally, attempts to modify existing systems to include equipmentto provide accurate seed counts have been economically unfeasible,requiring line retooling and capital investment and utilizing systemsgenerating stale data. The current systems require, in some cases, asmuch as 30 minutes to determine the applicable density data. In suchcases, by the time the density date is available, the density of thepassing product may have substantially deviated from the determination,providing data of little utility.

Moreover, it is sometimes desirable to obtain, or retain, samples ofsmaller quantities for assessment. Such sampling generally requiressmall discrete samples.

Thus, there is a need in the art for a system for use in productprocessing operations that rapidly determines the density of products,which can do so by eliminating broken products from the count, countingthe products within clusters, and counting products of varying sizes andwhich, when desired, may also be used to obtain a desired product countper bag with little waste. Ideally, there is a need for a system whichmay be integrated easily into existing operations and the currentproduct handling systems without excessive line retooling and withoutsubstantial capital investment or which may be used for laboratory ortesting assessments. There is also a need for a system which integrateswith the current plant information and control systems along with theweigh-bagger to provide an accurate method for dispensing a weight thatcontains a very accurate number of objects (seeds), particularly onedesigned to work in-line and support high volume operations.

SUMMARY OF THE INVENTION

The present invention therefore meets the above needs and overcomes oneor more deficiencies in the prior art by a system that rapidlydetermines the density of products and, which may be used for assessmentor to obtain a desired product count per bag with little waste, andwhich can do so by eliminating broken products from the count, countingthe products within clusters, and counting products of varying sizes.Moreover, the present invention provides a system which may be used inlaboratory or test environments or which integrates easily into existingoperations and the current product handling systems, thus reducing lineretooling and reducing the total capital investment. The system may beintegrated with the current plant information and control systems alongwith the weigh-bagger to provide an accurate method for dispensing aweight that contains a very accurate number of objects (seeds). Whereintegrated for production use, the system is designed to work in-lineand support high volume operations. Thus, the invention provides systemsand methods for determining the product density value of preferablesmall fungible products within an acceptable size range within anacceptable color range

The system for determining the product density value of preferable smallfungible products within an acceptable size range may comprise a weightscale, an imaging table, a camera, a counting processor, and a densityprocessor. The weight scale may be adapted to determine a sample weightvalue indicative of the weight of a sample. The camera may be adapted totransmit at least one image, having at least two color ranges or havinga specific color range, of said imaging table to the counting processorwhere the counting processor is adapted to identify each acceptableproduct in said sample within said acceptable size range and determinethe number of acceptable products. The counting processor may be adaptedto identify at least two counts within said number of said acceptableproducts according to two or more colors, to perform a morphologicalerosion on said at least one image of said products above saidacceptable size range until all of said products above said acceptablesize range appear separated or in predictable count clusters, and todetermine the number of said separated products and the number ofproducts in said predictable count clusters. The counting processing mayfurther be adapted to combine the number of acceptable products withinthe sample and within the acceptable size range and the number ofseparated products to determine a sample count. The density processor isadapted to receive an input of the sample weight value and to determineproduct density value by dividing the sample count by the sample weightvalue.

Thus, the present invention includes a container which defines a sampleof products, a framework, a scale, a processor, an imaging table, and anassociated camera. The system may be connected to an automatedbagger/scale, a display for a manually-operated bagger/scale, or to aplant computer system for bagging, quality control, or record keeping.The invention accurately weighs a sample of product with a high degreeof precision, accurately counts the quantity of product in the sample,and determines the value of the product density of the associated andlarger zone of the product flow. Determination of the value of theproduct density provides several benefits. Where a desired quantity isto be dispensed, once the product density value is known for aparticular zone of product flow, the desired minimum weight necessary toobtain the desired product count from that zone may be determined and abagger/scale controlled to obtain that minimum weight.

In operation, the method determines the product density value ofpreferable small fungible products within an acceptable size range andwithin an acceptable color range, by 1) obtaining a sample of mixedproducts; 2) determining a sample weight of said sample; 3) imaging saidsample to produce at least one image having at least two color ranges;4) processing said at least one image to identify and count theindividual preferred small fungible products within said acceptable sizerange and within said acceptable color range, to identify areas of saidimage containing objects larger than the small preferred small fungibleproducts within said acceptable size range and within said acceptablecolor range, and to retain only said areas of the at least one imagecontaining objects larger than the preferred small fungible productswithin said acceptable size range and within said acceptable colorrange; 5) repeatedly processing said at least one image tomorphologically erode said objects larger than the preferred smallfungible products within said acceptable size range and within saidacceptable color range, to identify the mean size of the eroded objects,determining an acceptable eroded object size about said mean size,processing said at least one image to identify and count the erodedobjects within said acceptable size and within said acceptable colorrange, and to retain only said areas of the at least one imagecontaining eroded objects larger than said acceptable size, until noeroded objects remain; 6) combining said count of the number of saidindividual preferred small fungible products and said count of thenumber of eroded objects within said acceptable size and within saidacceptable color range to produce a sample count; 7) determining saidproduct density value by dividing said sample count by said sampleweight value; and 8) outputting said determination of product densityvalue.

Unlike prior inventions, the invention images or takes a picture of thesample in a two tone, black-and-white, or dichromatic image, countsproducts within a size range, and then uses a morphological process toidentify and count products in clusters or products larger than theproduct range. The system allows the images to be saved in digitalformat to permit future retrieval for use in plant audits and historicalvalidation of the material processes at any given time.

In another embodiment a color camera is used and the color of theproduct can also be assessed. Applications for this embodiment includethe simultaneous counting of the seeds in a sample and thecategorization of those seeds into two or more subgroups, such asoff-color versus prime, or resistant versus refuge based on treatmentcoating color in refuge-in-bag seed packaging operations.

In yet another embodiment, the present invention a scale, a processor,an imaging table, and an associated camera. for laboratory applicationwhere samples are fed manually by an operator. The system can bepackaged as a bench top unit to provide all of the rapid analysis anddata connectivity advantages of the on line unit, but as a labinstrument.

Additional aspects, advantages, and embodiments of the invention willbecome apparent to those skilled in the art from the followingdescription of the various embodiments and related drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the described features, advantages, andobjects of the invention, as well as others which will become apparentare attained and can be understood in detail; more particulardescription of the invention briefly summarized above may be had byreferring to the embodiments thereof that are illustrated in thedrawings, which drawings form a part of this specification. It is to benoted, however, that the appended drawings illustrate only typicalpreferred embodiments of the invention and are therefore not to beconsidered limiting of its scope as the invention may admit to otherequally effective embodiments.

In the drawings:

FIG. 1A is an illustration of one embodiment of the present invention inconnection with an existing product bin, bagger/scale, and bag.

FIG. 2 is a flowchart of steps of the present invention for use with anexisting product bin.

FIG. 3 is an illustration of another embodiment of the present inventionin connection with an existing product bin, bagger/scale, and bag.

FIG. 4 is an illustration of a scale which used in the presentinvention.

FIG. 5 is an illustration of the imaging table which used in the presentinvention.

FIG. 6 is a flowchart of the steps used associated with the processingof an image from the imaging table.

FIG. 7 is a graphical depiction of an image from the imaging table aspart of morphological processing.

FIG. 8 is a graphical depiction of an image from the imaging table aftermorphological processing.

FIG. 9 is an illustration of a damping device which may be placedintermediate the scale and the imaging table.

FIG. 10 is an illustration of a parallel flow system with the presentinvention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring to FIG. 1, the invention provides systems and methods fordetermining the product density value of preferable small fungibleproducts within an acceptable size range within an acceptable colorrange. In particular, the invention includes a system which includes asample input piping 102 from a bin or piping 104 of products 106, suchas selected preferable small fungible products and which may be a mixedflow further including undesirable contaminants, through which products106 flow, a sampling volume 108, for defining a sample 140, a weightscale 110, a processor 112, an imaging table 114 and an associatedcamera 116. The invention may include a sample output pipe 118, and mayinclude or be associated with a bagger/scale 120. Operation of thesecomponents provides for successively sampling of each zone 122 in thebin 104 to determine the quantity of acceptable product per unit weight.While this data may be used for later historical review, it may be alsobe utilized with a bagger/scale 120 particularly to determine a desiredweight of product for that zone equivalent to the desired productquantity, and to terminate operation of a bagger/scale 120 when thedesired weight of the product 106 at the bagger/scale 120 is reached,thus adjusting the weight of the products 106 dispensed into each bag124 containing the products 106 based on the associated zone 122. Suchuse may require a dedicated processor or a site-based computer network.If so used, the weight of the bags 124 of the products 106 dispensed bya bagger/scale 120 will vary over time and, dependent on source of theproducts 106 in that zone while the product count for each bag 124 willbe roughly equivalent. This system therefore can compensate forvariations among supplying entities where product supplies aresubsequently piled atop one another.

Still referring to FIG. 1, the sample input piping 102, which may be apipe, or a channel or other structure to communicate seeds, is incommunication with the bin or piping 104. In one embodiment the sampleinput piping 102 penetrates through the side wall of the bin or piping104 to the center of the sample input piping 102, where the sample inputpiping 102 terminates in an upward opening 126. Thus, as each zone 122of the products 106 moves downward a representative sampling of the zonepasses into the sample input piping 102, a flow diversion step 202identified in FIG. 2. The sample input piping 102 has a first end and asecond end, with the sample input piping 102 adapted for communicationwith the sample volume 108 at the second end of the sample input piping102. The sample input piping 102 is also adapted for communication withthe bin 104 of products 106, particularly where the products 106 are inone or more of said zones 122 in the bin 104. In such situations, theproducts 106 in each of the one or more of said zones 122 generally havenearly equivalent characteristics of size, weight and percentage ofdesired products. A gate 128 may be located at the upward opening 126 tofurther control the flow into the sample input piping 102 but may alsobe omitted. The sample input piping 102 is also in communication with asampling volume 108 which may include a top gate 134 at its top side 130and a bottom gate 136 at its bottom side 132, defining a samplingcontainer. The sampling container has an internal volume, which, whenfull, defines the sample by defining the sampling volume 108. The sampleinput piping 102 must therefore have a sufficient sized opening to drawfrom a zone 122 of the bin or piping 104. The sample input piping 102ideally operates on a gravity feed, downwardly descending as it passesout of the bin or piping 104. The sample input piping 102 may be incontact with or connected to an eccentric or vibratory motor or othervibration-inducing device, such as a vibratory feeder 150, which may beconnected to the sample input piping 102 to ensure the products 106 donot bridge, or stack atop of or in the sample input piping 102.

In operation, closing the bottom gate 136, step 204 of FIG. 2, definesthe bottom of the sampling volume 108 and closing the top gate 134prevents the addition of further products 106 into the sampling volume108, and therefore defines the sample 140 when filled with products 106,particularly the volume of the sample 140. As can be appreciated, it isessential that the flow of the products 106 be metered to control thevolume of the products 106 introduced to the present invention as asample 140. The top gate 134 may be closed, step 206 of FIG. 2, based ona pre-determined point in operation, which may by time, or a switchlocated in the sampling volume 108. The volume of the sample 140 may bedefined by lengthening the sampling volume 108 or by increasing ordecreasing the usable interior volume of the sampling volume 108, suchas by inserts or interchangeable sampling volumes 108. The sample 140may be collected at the time determined by a processor 112.

Referring to FIG. 3, a return pipe 302 may be in communication with thesample input piping 102 above the top gate 134 to provide a return tothe bin or piping 104 for any products 106 prevented from entering thesampling volume 108, step 208 of FIG. 2. The product 106 contained inreturn piping 302 may be directed to another part of the bin 104 viaconnection of return piping 302 to sample output piping 118 or processedotherwise. Alternatively, the return piping 302 may terminate in anopening 304 in the bin 104 which may be positioned to ensure return ofthe products 106 to the same zone 122 from which it was sampled at thetime the zone 122 reaches the opening 204.

Returning to FIG. 1, in the first embodiment, the sampling volume 108 isalso in communication with a scale 110, such that when the bottom gate136 of the sampling volume 108 is opened, after the sampling volume 108has filled with the products 106 and fixed a sample 140, the products106 of sample 140 contained therein falls onto the scale 110, step 210,which weighs the sample 140 and provides an output to a processor 112,step 212 of FIG. 2, consistent with, and indicative of, the weight ofthe sample 140. Scale 110 is therefore adapted to transmit a sampleweight value to a processor 112, which may function as a weightprocessor. The scale 110 may therefore be positioned below the samplingvolume 108 and receive the sample 140 from the sampling volume 108.

Referring to FIG. 4, the scale 110, preferably constructed to include abucket, is preferably rotatably mounted to permit the sample 140contained in the scale 110 to be dispensed onto an imaging table 114after the processor 112 records the weight associated with sample 140,step 212 of FIG. 2. Scale 110 may therefore be a dump scale. The scale110 may be hingely mounted by pins 402 connected to a support frame 408and maintained in position by a rotatable or releasable arm 404connected to a motor or by piston 406 or may be rotatably mounted androtated about an axis. Alternatively, the scale 110 may be fixed inposition and associated with a closable dispenser, thereby opening andclosing the orifice to permit the products 106 associated with thesampling volume 108 in the scale 110 to be dispensed onto an imagingtable 114. In a further alternative (not shown), a brush or plow may beassociated with scale 110 to push the products off the scale 110 fordelivery to the imaging table 114. The imaging table 114 may bepositioned below the scale 110 and positioned to receive the sample 140from the scale 110. In each instance, the scale 110 is preferablyemptied in response to a signal from the processor 112, but may beconstructed to empty after a uniform time period. For accuracy, thescale 110 preferably is accurate to at least one hundredth of a gram.

Due to the position of the products 106 in the sample 140 in the scale110, products 106 may have increasing potential energy which may betranslated to kinetic energy during the dispensing from the scale 110 tothe imaging table 114. Thus it may be helpful to include a dampingdevice between the scale 110 and the imaging table 114, such as thedamping device 902 depicted in FIG. 9, which dissipates the kineticenergy of the products 106 prior to reaching the imaging table 114. Thismay be accomplished by, among other options, a damping device 902 whichincludes a number of protrusions, such as spaced apart bars or pegs 904,as illustrated in FIG. 9. Such protrusions absorb some of the energy ofthe moving products 106 while only slightly slowing the flow of theproducts 106 in the sample 140 to the imaging table 114. Alternatively,the damping device 802 may include other materials intended to contactproduct 106 during its downward descent and thereby slow the product106, such as rotating paddles or ribbons of material. Alternatively, thedamping device may include a textural profile using long screws withplastic heads to retard the velocity of the falling products 106.

Referring again to FIG. 1, once the products 106 of the sample 140 aredeposited on the imaging table 114, step 214 of FIG. 2, the sample 140,namely the products 106, are imaged by the camera 116, preferably amonochromatic camera of sufficient resolution to identify the edges ofindividual products 106, step 216 of FIG. 2. Camera 116 may bedichromatic, black-and-white, or color, or permit vision of two or morecolor ranges. Preferably, the imaging table 114 is illuminated frombelow, thus providing high contrast between the surface 138 of theimaging table 114 and the products 106 of sampling volume 108.

In an alternative embodiment, the scale 110 and the imaging table 114are integrated into a single unit, such that there is no need for step214 to dispense the sample 140 from the scale 100 to the imaging table114.

The processor 112 receives one or more images of the imaging table 114via the camera 116 from which a count of acceptably-sized products 106contained in sample 140 is determined, step 218 of FIG. 2. Camera 116must therefore be adapted to transmit at least one image of the contentof the image table 114 to a processor 112, which may be a countingprocessor. The counting processor is adapted to distinguish the productsof said sample within a desired color range, to eliminate fromidentification those products outside the desired color range, and maybe adapted to identify at least two counts within the identified numberof acceptable products according to two or more colors.

In an embodiment where camera 116 is color camera, at step 218 theprocessor 112 may further include, within the count of acceptably-sizeproducts 106, a count of product within the sample meeting colorcharacteristics, which may result into categorization of products 106into two or more subgroups, such as off-color and prime, or intoresistant and refuge based on treatment coating in refuge-in-bag seedpackaging operations. Color count may be desirable where seed color isindicative of desired product 106 versus undesirable product ofacceptable size. Use of a color camera permits distinguishing amongproduct, for example, between modified and refuge kernels based on thecolor of the treatment coating. Further, it becomes possible in such asituation to weigh and count all of the kernels, and to assess thered:green ratio to determine whether it is coated red or green. In othercircumstances, a monochromatic camera provides advantages over a colorcamera as there is no interpolation of pixel values across the Bayerfilter inputs. The benefits of monochromatic images are noticeable insoybeans and wheat. In such circumstances, the 8-bit grayscale image isconverted to binary (1 or 0) in the threshold step. If the image iscolor, one of the three planes (R, G or B) is selected and that 8-bitgrayscale image is also processed into a binary image by thresholding.The system may further be adapted to use color to assess the defectlevel (such as off-color product or foreign material) in the productflow, particularly if product is alternatively imaged from top andbottom for comparison purposes, in which embodiment, the product couldbe illuminated from below, rather than from above, for counting ofproduct, then illuminated from the opposite side so the system couldapply an overlay, created from the first image, with color added to eachobject location and assess the color for each object.

Moreover, at step 218, the method may further include display of thesample count and the display of the image from the camera 116 andadjustment of the sample count, such as by the operator, prior todetermination of the product density value. Thus, the operator mayoverride the counting processor if needed.

In an alternative embodiment, the scale 110 and the imaging table 114are integrated into a single unit, such that there is no need for step214 to dispense the sample 140 from the scale 100 to the imaging table114.

In another alternative embodiment depicted in FIG. 10, one or moreseparate pipings 1002 of products 106 may be operated in parallel withpiping 104. Thus, multiple paths of products 106 may be simultaneouslyused and utilized, all relying on the data from the first path of piping104.

In operation, the processor 112 determines the number ofacceptably-sized products 106 on the imaging table 114 from an imagereceived from the camera 116, based on identification of the edges ofthe products 106 on the imaging table 114, which identify products 106within an acceptable size range. The acceptable range of sizes ofproducts 106 may be defined in the processor 112 based on the productbeing dispensed, or may be determined based on the size of imagedproducts, i.e., those within a range of sizes within a deviation,preferably those within one standard deviation of the mean size.

The identification of the size of the products 106 is accomplished, inpart, due to the construction of the imaging table 114. As depicted inFIG. 5, the imaging table 114 includes a surface 138, which is at leasttranslucent to light and which is illuminated from below. Thisillumination may be from any light source 504, but preferably one thatprovides a relatively consistent and sufficiently high level ofillumination. Preferably the light source 504, such as a light,illuminates the imaging surface 138 of the imaging table 114 (an imagingtable surface), preferably from above, but if a transparent imagingsurface 138 is employed, then alternatively from below. The light fromlight source 504 is preferably diffused at the imaging surface 138 toprovide consistent illumination. This diffusion may be accomplished by adiffuser 506 integrated into the surface 138 or below it. As a result,the diffused illumination at the surface 138 of the imaging table 114,when covered with the products 106, provides a dichromatic image whereinthe products 106 appear black against a white background. Additionally,the light source 504 and the reflected light entering the camera 116 maybe cross-polarized to eliminate glare from the imaging table 114 or theproducts 106. To reduce clumping or layering of the products 106 in thesample 140, the surface 138 of the imaging table 114 may be associatedwith a vibrating device, such as eccentric motor, thus causing thesurface 138 of the imaging surface 138, and the products 106 thereon, tovibrate and thus separate the products 106 from one another to avoid theproducts 106 clumping together or climbing atop one another.

The imaging surface 138 of the imaging table 114 may include an inclinedtransparent lip or ridge 516 about the surface perimeter 514 of theimaging table 114, on which the products 106 cannot rest, to betterprovide an extensive translucent surface for the imaging surface 138 andavoid the potential for the edge of a product 106 to be adjacent anon-translucent surface such as the edge of the imaging table 114, whichwould create difficulty in identifying the edges of the products 106.Moreover, the inclined transparent lip or ridge 516 may be raisedsufficiently, or may have extended sides, to prevent the products 106from bouncing off the imaging table 114 when transferred from the scale110. Additionally, the imaging table 114 may include one or more airjets 518 aimed the imaging surface 138 at or near the corners of lip orridge 516 to better force products 106 away from the edges of imagingtable 114. The vibratory motor 520 or other device may also be used toshift the products 106 about the imaging table 114 between images fromcamera 116, thus providing a different presentation of products 106 forsubsequent review. The vibratory motor 520 or other device may beconnected to the imaging table 114.

Referring to FIG. 1, the acceptable-size of the products 106 used foridentification may be pre-programmed, or may be determined by theprocessor 112 as the mean size of the products 106 initially identifiedby the processor 112 upon review of data from camera 116.

Additionally or alternatively, the imaging surface 138 of the imagingtable 114 may be illuminated by a light 142 for assessment of theproducts 106 deposited on the imaging table 114. Preferably light 142provides broad spectrum lighting, which may generally be white light,and may be characterized as warm white light. Alternatively, light 142may provide light in as few as two wavelengths Light providing aplurality of wavelengths is beneficial as various products 106 mayreflect at different wavelengths when exposed to broad spectrumlighting. Foodstuffs, for example, contain more red than blue. It isimportant light 142 provide each of the red, green and blue wavelengthsso that all pixels filled by a camera 116 respond to the sample 140being viewed.

When used, the data from the camera 116 is assessed by processor 112 toidentify those areas of the imaging table 114 which are covered by anobject sufficiently different in color, which may be bad products (suchas rotted seed), rocks, or other contaminates. They may be seeds ofdifferent genetic properties indicated by different colors of coating.These objects can be subtracted or eliminated from the image byprocessor 112 before identification or assessment of the mean productsize and/or the counting of products.

Returning to the product count, in determining the product count,clusters of products, which generate an image clearly beyond theaccepted distribution from the acceptable product size, are not counted.Similarly, broken products or other undesirable constituents, to theextent not already removed, will not be counted to the extent they arebelow the accepted distribution from the acceptable seed size. This isaccomplished by processing of the image by processor 112, which isadapted to identify each acceptable product 106 in the sample 140 withinthe acceptable product size range and to determine the number ofacceptable products 106. In one embodiment, the raw image of the sample140 from camera 116 may be converted to a binary image having athreshold value, such as 30. The binary image may then be processed tofind all connected regions, and to identify all isolated products 106and clusters of four or more products 106.

Referring to FIGS. 1 and 6, once those products 106 fitting within theacceptable product size range are counted, step 604 of FIG. 6, processor112 filters the image, first removing the image of those products 106which were counted and those that fall below the acceptable productsize, step 606, i.e., subtracting those areas, and then using a knownmorphological technique, such a erosion, to reduce the size of theproducts 106 in the remaining product clusters in the image, step 608,until the image of the products 106 is sufficiently eroded for a furthercount, step 610, i.e. to perform a morphological erosion on the image ofthose products 106 above the acceptable size range until all of theproducts 106 above said acceptable size range appear separated or inpredictable count clusters. The processor 112 is thus able to determinethe number of separated products 106 and the number of products 106 inpredictable count clusters. In a further embodiment, the processor 112may be adapted to mark the eroded products as discrete individuals, thendilate the image using the same structuring factor to return theidentified product 106 to their original size, but recognized asdiscrete products 106. Thus, in the further embodiment, theremove-singles-and-erode process may be performed but seeing thepreviously marked products 106 as full size. In an alternativeembodiment, a dilation step may be added after the products 106 areseparated, providing the advantage of restoring the products 106 tooriginal size in the original image. While not essentially, without thisstep, the exact size and shape used in the analysis will vary based onhow much the erosion step 608 has reduced the object. Depending on thesurroundings, two clusters of the same size may erode differently andhave differing, potentially only slightly, geometric characteristics.Processor 112 must therefore be adapted to at least perform amorphological erosion on the image of the products 106 outside theacceptable size range until at least some of the products 106 above theacceptable size range appear separated and be adapted to determine thenumber of separated eroded images of products 106. Clumps or collectionsof products 106 are therefore reduced to individual product images. Inone embodiment, this may be accomplished by eroding the image by astructuring factor of 4.0, to provide nearly complete isolation ofindividual objects, leaving potentially only a few non-singulatedproducts 106, small enough to be characterized in the count algorithm.The processor 112 then counts the identified products 106, step 612,repeating the process of erosion, step 608, assessment, step 610, andcounting products 106 on the image, step 612, on the image until allproducts 106 have been removed. Ideally no more than six repetitions areperformed on an image due to time constraints. Processor 112 may thencombine the number of acceptable products 106 within the sample 140within the acceptable size range, the number of separate productsidentified by the erosion, and the number of products in predictablecount clusters, to determine a sample count.

To reduce error, the process, steps 606-612, may be repeated on afurther copy of the image or multiple images recorded or photographstaken, potentially with different structuring elements to providepotentially differing product counts. A statistical point in thedistribution of the identified product count(s) may then be used, whichmay be the mean, a point below the mean, thus providing a higherapproximate product count, or a point above the mean, thus providing alower product count.

Where the morphological operation used is erosion, pixels are removed onobject boundaries. As is known, the number of pixels added or removedfrom the objects in an image depends on the size and shape of thestructuring element used to process the image. For most products acircular matrix is sufficient; however in some instances a perpendicularintersection of two lines is better. The latter, for example, is helpfulin erosion of an image of corn kernels, largely due to the variation inkernel size and irregular geometry. In cases of uniform geometry such aswith soybeans, some portion of the objects in the view may be isolatedand fall within the acceptable size range. These objects are counted assingles and can be removed from consideration before the erosion processbegins. The remaining objects will be eroded and reevaluated. At thispoint, size alone is not sufficient to determine the count because acluster of two seeds may have been eroded to an area within theacceptable size range. Other geometric properties must be used todetermine the object count. It is well known that the relationshipbetween area and perimeter can be useful in evaluating geometry.Roundness is also a powerful discriminator. Threshold values can beestablished for reliable discrimination between clusters of two, threeor four seeds. Larger clusters may not be reliably counted, so anadditional erosion step may be applied and the algorithm applied to theresulting images. This process may be repeated until the largest clusteris small enough to be reliably measured. In the specific case of mediumsize soybeans imaged at a scale of 75 pixels per inch, the algorithm[[perimeter/roundness squared]×[area/1000]] yields a parameter thatreliably predicts the number of seeds in a cluster. Parameter valuesbelow 6 indicate a single seed, between 6 and 75 two seeds, between 75and 150 three seeds between 150 and 500 four seeds. Values above 500indicate a larger cluster and the need for more erosion.

For object which are not uniformly round the parameter roundness may notbe a useful discriminator. Other geometric properties such as axisratio, the relationship between the largest inner circle and thesmallest outer circle have been shown to improve discrimination.

While there are some common elements in the algorithm selection for aspecific seed based on its shape, each seed such as soybean, corn,cotton, pumpkin, rice, etc. each must be evaluated independently todetermine the most effective algorithm.

In cases where the geometry is very irregular another approach may beneeded. The processor 112 may assess each pixel or grid section in theimage based on the surrounding pixels. The grid size applied by theprocessor is defined by the user and is typically a grid created by twoperpendicular axes. As depicted in FIG. 7, because of the contactbetween the various products 106, and therefore the edges touching andforming one continuous object, on the imaging surface 138, processor 112may initially determine only two products are present. As depicted inFIG. 7, when processor 112 assesses cell 742 based on a circular matrix,it considers whether all cells 731, 732, 733, 741, 743, 751, 752 and 753contain data, part of a product 106, a binary “1”. As cell 741 does notcontain data but rather is empty, the value of cell 742 is determined tobe set to zero upon completion of the assessment; i.e., all dataremoved, of the entire image, thus eroding the edge of product 106associated with cell 742 in the revised image. The processor 112 thencontinues across the image to the next cell, 743, and assesses the cellbased on the original image. The processor 112 then assesses the erodedimage to determine the number of products 106 present, which have beenreduced in size and, hopefully, separated from the former clusters byerosion of the associated edges of the products 106 in the cluster. Theresult is identification of the products present, such as on FIG. 8,which after erosion through one or more iterations, separates theproducts 106 to identify the actual count of ten (10) products 106. Forthose clusters not separated by the first erosion, further erosions maybe performed on the image(s) until all clusters of products have beenreduced to individual product images. The speed and accuracy of thiserosion can be adjusted based on the resolution of the camera 116 usedand the size and configuration of the matrix used for erosion.

Additionally, where one or more clusters, such as three product cluster,of product 106 is identified, the eroded image may be altered,potentially reblobbed, such that the previously connected objects arenow identified as separate objects and then dilated back to the previousimage. The products 106 touching one another can still be recognized asindividuals and counted.

The few non-singulated products 106, generally clusters of two or moreproducts 106, can be evaluated based on the count algorithm, where thediscriminator is a function of perimeter, roundness and area, such asperimeter/roundness/roundness*area/1000/. For example, given a two-seedobjection having a perimeter of 63.11, roundness of 0.700 and area 232,the calculation of perimeter (63.11) divided by roundness (0.700)divided by roundness (0.700) times area (232) has a value of 29.86,which if used in connection with a 2:3 threshold of 75, will be countedas two seeds. Similarly, given a four-seed object having a perimeter of117.4, roundness of 0.606 and an area of 502, the calculation ofperimeter (117.40) divided by roundness (0.606) divided by roundness(0.606) times area (502) has a value of 160.41, above a 3:4 value of 150and thus counted as four seeds.

The system may provide a visual confirmation to the operator, where theoriginal image is presented with an overlay showing the countedclusters, which may be color coded to identify the number of products106 determined to be present in a cluster. The operator may, afterreviewing the data, determine a threshold differentiating betweenadjacent cluster sizes is inaccurate and should be adjusted. This isparticularly true when the system is first being calibrated for aparticular product 106.

Referring to FIG. 5, imaging table 114 is preferably rotatably mountedto permit the products 106 associated with the sampling volume 108imaging table 114 to be dispensed to a sample output piping 118, withwhich imaging table 114 is in communication, after processor 112determines the count of products 106 of sampling volume 108, step 220 ofFIG. 2. Imaging table 114 may be hingely mounted on pivots 508 connectedto a support frame 522 and maintained in position by a piston 510attached to the imaging table 114 and the support frame 522 or may berotatably mounted and rotated about an axis. In a further alternative, abrush 512 or plow may be associated with imaging table 114 to push theproducts 106 off the imaging table 114 for delivery to the sample outputpiping 118 or to ensure all products 106 are removed from the imagingtable 114. In each instance, the imaging table 114 is preferably emptiedin response to a signal from the processor 112, but may be constructedto empty after a uniform time period.

Referring to FIG. 1, after products 106 of sampling volume 108 areremoved from the imaging table 114 and communicated to sample outputpiping 118, which may return the products 106 to the bin or piping 104.

In an alternative embodiment, the invention may be a unit constructedfor use in a laboratory application, thus providing the same databenefit, but driven by an operator.

Returning to FIGS. 1, 2, and 3, once a count of products 106 in theweight of sample 140 is known, the product density value of the products106 associated with a zone, such as zone 122 is established by dividingthe sample count, step 220 of FIG. 2, by the sample weight, step 212 ofFIG. 2, which is accomplished at step 222 of FIG. 2. Density may bedetermined in a processor 112, which may be a density processor adaptedto determine the product density value of a zone of preferable smallfungible products within an acceptable size range by dividing the samplecount by the sample weight value. Beneficially, as the time forobtaining the sample weight, step 212 of FIG. 2 and the sample count,step 220 of FIG. 2 can be quite short, the product density value may beobtained rapidly, such as nearly instantaneously, which may also bereferred to as obtaining the product density value in real time, or innear real-time. The density processor may be adapted to receive an inputof the sample weight directly from the scale 110 or may be received fromother input devices, such as a keyboard or touch-sensitive device, suchas a touch screen.

As a result, where the invention is integrated into a flowing productsupply, the product density value of a zone 122 in bin 104 may bedetermined in less than a minute, and preferably the value of productdensities of three nearby zones 122 may be obtained within a minute.Desirably, the time frame should be less than five seconds. Mostparticularly, the product density value of a zone 122 in a flow ofproducts 106 is ideally determined and output to a product flowcontroller 144 controlling a flow control device, such as thebagger/scale 120 or a gate, before the zone 122 reaches the flow controldevice, thus providing the product density value in real time. Whenneeded, a desired minimum weight associated with the desired quantitymay be obtained by dividing the desired quantity by the product densityvalue, step 224 of FIG. 2. In those instances when an automated baggeris associated with and directly connected to the invention, which is notrequired, when the products 106 of the zone 122 associated with thesampling volume 108 reach the bagger/scale 120, the processor 112, whichmay be a bag-weight processor, activates the bagger/scale 120 andreceives a signal from bagger/scale 120 associated with the weightreading output from the bin or piping 104, step 226 of FIG. 2. Theprocessor 112, as a bag-weight processor, is adapted to determine thedesired weight associated with a desired quantity of product by dividingsaid desired quantity by said product density value. When the weightreading output from the bagger/scale 120 to the processor 112 reachesthe weight associated with the desired product count, bagger/scale 120ceases to feed product 106 to the bag 124, step 228 of FIG. 2. Thus, thebagger/scale 120 is adapted to transmit the actual bag weight to theprocessor 112, which is adapted to compare the actual bag weight to saiddesired weight. The processor 112 is further adapted to terminateoperation of the bagger/scale 120 when the actual bag weight isequivalent to the desired weight.

A larger product plant-based system may alternatively receive theproduct density value data and via a product flow controller 144 controlthe bagger/scale 120. Thus the density calculation may be accessed by aplant information system or an automated packaging system. Similarly,the desired weight may be displayed on a display associated with amanual bagger, permitting the operator to feed the correct weight ofproduct into the bag. Further, the data associated with a zone 122, andtherefore with a product from a particular supplier, may be retained ina product plant-based system for historical purposes or quality control,such as average size, quality of product, or percentage of contaminants,thus providing. storage of at least one image of a sample. Thus, theimaged information may be stored for future analysis, audit support, andprocess improvement activities in a storage component, such ascomputer-readable media, such as hard drives, diskettes, and flashmemory. Additionally, the system may provide storage of said productdensity value for access by a plant information system or an automatedpackaging system. With such data, the plant operator can better selectsuppliers and ensure higher quality product and lower contamination,which slows processing and increases cost.

As can be appreciated, this weight and imaging process permits theproduct density value to be determined several times per minute,resulting in data in real-time or near real-time, i.e., at approximatelythe same time the product 106 passes through the system withoutsubstantial delay, thus permitting the operation of any equipment on theproduct flow line, such as a bagger/scale 120, to be operated at thetime the zone 122 associated with the sample 140 reaches the equipment,thus avoiding or addressing potential variations of product densityvalue in various zones 122 in bin or piping 104.

Alternatively, the invention may be embodied in laboratory or testenvironment such that the plant-specific components are not included,such as the bagger/scale 120. Instead, the system may be reduced insize, retaining the required components, and thus providing assessmenton the fly of desired samples.

The terms and expressions which have been employed in the foregoingspecification are used therein as terms of description and not oflimitation, and there is no intention, in the use of such terms andexpressions, of excluding equivalents of the features shown anddescribed or portions thereof. It will be evident to those skilled inthe art that various modifications and changes can be made theretowithout departing from the broader spirit or scope of the invention.Accordingly, the specification is to be regarded in an illustrativerather than a restrictive sense. It is therefore, contemplated thatvarious alternative embodiments and modifications may be made to thedisclosed embodiments without departing from the spirit and scope of theinvention defined by the appended claims and equivalents thereof.

We claim:
 1. A system for determining the product density value ofpreferable small fungible products within an acceptable size range,comprising: a weight scale, said weight scale adapted to determine asample weight value indicative of the weight of a sample; an imagingtable; a camera, said camera adapted to transmit at least one image ofsaid imaging table to a counting processor; said counting processoradapted to identify each acceptable product in said sample within saidacceptable size range and determining the number of said acceptableproducts, said counting processor adapted to perform a morphologicalerosion on said at least one image of said products above saidacceptable size range until all of said products above said acceptablesize range appear separated or in predictable count clusters and ofdetermining the number of said separated products and of the number ofproducts in said predictable count cluster, and adapted to combine thenumber of said acceptable products of said sample within said acceptablesize range, said number of said separated products to determine a samplecount, and said number of products in said predictable count cluster;and a density processor adapted to receive an input of said sampleweight value and to determine said product density value by dividingsaid sample count by said sample weight value.
 2. The system of claim 1,further comprising: a light illuminating said imaging table surface andwherein said counting processor is adapted to distinguish the productsof said sample within a desired color range and adapted to eliminatefrom identification those products outside the desired color range. 3.The system of claim 1 further comprising: storage of said at least oneimage of each of said sample.
 4. The system of claim 1, wherein saidimaging table includes an imaging table surface, said imaging tablesurface at least translucent to light, said imaging table surfaceilluminated from below said imaging table surface.
 5. The system ofclaim 4 wherein said imaging table includes an inclined transparent lipabout said imaging table surface.
 6. The system of claim 1, furthercomprising: a support frame, said imaging table hingedly affixed to saidsupport frame, and a piston, said piston affixed to said weight scaleand said support frame.
 7. The system of claim 1 further comprising:storage of said product density value for access by a plant informationsystem or an automated packaging system.
 8. The system of claim 1further comprising: a sampling container, said sampling container havingan internal volume, said internal volume of said sampling containeradapted to define said sample; said weight scale positioned below saidsampling container and positioned to receive said sample from saidsampling container, said weight scale adapted to transmit said sampleweight value to said weight processor; and said imaging table positionedbelow said weight scale and positioned to receive said sample from saidweight scale.
 9. The system of claim 8, wherein said sampling containerfurther comprises a top gate and bottom gate.
 10. The system of claim 9,further comprising: a sample input piping, said sample input pipinghaving a first end and a second end, said sample input piping adaptedfor communication with said sampling container at said sample inputpiping second end, said sample input piping adapted for communicationwith a bin of products, said products in one or more of said zones insaid bin, said products in each of said one or more of said zones havingnearly equivalent characteristics of size, weight and percentage ofdesired products.
 11. The system of claim 10, further comprising: avibratory feeder, said vibratory feeder connected to said sample inputpiping.
 12. The system of claim 8, wherein said weight scale is a dumpscale.
 13. The system of claim 8, further comprising: a vibratory motor,said vibratory motor connected to said imaging table.
 14. The system ofclaim 13, wherein said imaging table further includes at least one airjet aimed at said imaging table surface.
 15. The system of claim 13,further comprising: a damping device to slow said product, said dampingdevice positioned between said weight scale and said imaging table. 16.The system of claim 15, wherein said damping device comprises aplurality of spaced-apart pegs.
 17. The system of claim 14, furthercomprising a bag-weight processor adapted to determine the desiredweight associated with a desired quantity of product by dividing saiddesired quantity by said product density value; a bagger, said baggeradapted to transmit the actual bag weight to said bag-weight processor,said bag-weight processor comparing said actual bag weight to saiddesired weight; and said bag-weight processor adapted to terminateoperation of said bagger when said actual bag weight is equivalent tosaid desired weight.
 18. The system of claim 1 where said densityprocessor is adapted to receive said sample weight value by manualinput.
 19. A method for determining the product density value preferablesmall fungible products within an acceptable size range within anacceptable color range, comprising: obtaining a sample of mixedproducts; determining a sample weight of said sample; imaging saidsample to produce at least one image; processing said at least one imageto identify and count the individual preferred small fungible productswithin said acceptable size range and within said acceptable colorrange, to identify areas of said image containing objects larger thanthe small preferred small fungible products within said acceptable sizerange and within said acceptable color range, and to retain only saidareas of the at least one image containing objects larger than thepreferred small fungible products within said acceptable size range andwithin said acceptable color range; processing said at least one imageto morphologically erode said objects larger than the preferred smallfungible products within said acceptable size range and within saidacceptable color range, to identify the mean size of the eroded objects;determining an acceptable eroded object size about said mean size;processing said at least one image to identify and count the erodedobjects within said acceptable size and within said acceptable colorrange; processing said at least one image to identify predictable countclusters and to determine the number of products in said predictablecount cluster; combining said count of the number of said individualpreferred small fungible products and said count of the number of erodedobjects within said acceptable size and within said acceptable colorrange to produce a sample count; determining said product density valueby dividing said sample count by said sample weight value; andoutputting said determination of product density value.
 20. The methodof claim 19 wherein processing said at least one image to identifypredictable count clusters and to determine the number of small fungibleproducts in said predictable count cluster comprises: determining anumber of small fungible products in each of said predictable countclusters based on the geometric properties of the eroded small fungibleproducts; and where the method claim 19 further comprises furtherprocessing said at least one image to morphologically erode saidpredictable count cluster; and determining a number of small fungibleproducts in each of said predictable count clusters based on thegeometric properties of the eroded small fungible products.
 21. Themethod of claim 19, wherein repeatedly processing said at least oneimage to morphologically erode said objects larger than the preferredsmall fungible products comprises: applying a grid to said at least oneimage to create a plurality of cells; selecting a grid cell from saidgrid; selecting a matrix of grid cells surrounding said grid cell;assessing whether said cells in said matrix of cells all contain data;selecting another grid cell from said grid; selecting a another matrixof grid cells surrounding said another grid cell; assessing whether saidcells in said another matrix of cells all contain data; deleting anydata from said grid cell if said cells in said matrix do not all containdata; and deleting any data from said another grid cell if said cells insaid another matrix do not all contain data.
 22. The method of claim 19further comprising: storing said product density value for access by aplant information system or an automated packaging system.
 23. Themethod of claim 19 further comprising: storing said least one image ofeach of said sample.
 24. The method of claim 19 further comprising:displaying said sample count; displaying said at least one image; andadjusting said sample count prior to determining said product densityvalue.
 25. A method for obtaining a desired quantity of preferred smallfungible products from a flow of mixed products, comprising: obtaining asample from a flow of mixed products; determining a sample weight ofsaid sample; imaging said sample to produce at least one image;processing said at least one image to identify and count the individualpreferred small fungible products within an acceptable size range andwithin an acceptable color range, to identify areas of said imagecontaining objects larger than the small preferred small fungibleproducts within said acceptable size range and within said acceptablecolor range, and to retain only said areas of the at least one imagecontaining objects larger than the preferred small fungible productswithin said acceptable size range and within said acceptable colorrange; processing said at least one image to morphologically erode saidobjects larger than the preferred small fungible products within saidacceptable size range and within said acceptable color range, toidentify the mean size of the eroded objects, determining an acceptableeroded object size about said mean size, processing said at least oneimage to identify and count the eroded objects within said acceptablesize and within said acceptable color range, and processing said atleast one image to identify predictable count clusters and to determinethe number of products in said predictable count cluster; combining saidcount of the number of said individual preferred small fungible productsand said count of the number of eroded objects within said acceptablesize to produce a sample count and within said acceptable color range;determining said product density value by dividing said sample count bysaid sample weight value; determining the desired weight associated withthe desired quantity by dividing said desired quantity by said productdensity value; activating a bagger associated with said flow of mixedproducts, said bagger transmitting an actual bag weight to said finalprocessor, said final processor comparing said actual bag weight to saiddesired weight; and terminating operation of said bagger when saidactual bag weight is equivalent to said desired weight.
 26. The methodof claim 25 further comprising: storing said product density value foraccess by a plant information system or an automated packaging system.27. The method of claim 25 further comprising: storing said at least oneimage of each of said sample.